CN116739455A - Artificial intelligence commodity circulation freight transportation tracking calculation terminal - Google Patents

Abstract

The invention discloses an artificial intelligent logistics freight tracking and calculating terminal which comprises an inventory management module, a path planning module, a transportation optimization module, a supply chain coordination module, a real-time monitoring module, a data analysis and prediction module and a risk management module, wherein the inventory management module, the path planning module, the transportation optimization module, the supply chain coordination module, the real-time monitoring module, the data analysis and prediction module and the risk management module are in communication connection, the inventory management module is used for managing the inventory of goods and comprises warehousing, ex-warehouse and inventory, and information acquired through the real-time monitoring module, the path planning module is used for calculating the shortest path or the optimal path according to the starting point and the ending point of the goods and the traffic condition factors of roads, and the transportation optimization module is used for calculating the shortest path or the optimal path according to the quantity, the volume and the weight factors of the goods; the invention has multiple effects of path planning, transportation optimization, data analysis, prediction, risk management and the like.

Description

Artificial intelligence commodity circulation freight transportation tracking calculation terminal

Technical Field

The invention relates to the field of logistics, in particular to an artificial intelligent logistics freight tracking and calculating terminal.

Background

With the development of science and technology, logistics gradually becomes a main way for people to transfer articles daily, and mainly the logistics companies transport articles in a transportation means and manpower mode, and with the universality of artificial intelligence, logistics gradually moves to intelligence, including logistics inventory, transportation and tracking.

However, the existing logistics tracking calculation terminal cannot plan a logistics path, so that the effect of optimizing the logistics path cannot be achieved, and meanwhile, mechanical energy data analysis, prediction and risk management cannot be achieved, so that logistics tracking management is not facilitated.

Disclosure of Invention

The technical problems to be solved by the invention are as follows: the existing logistics tracking calculation terminal cannot plan a logistics path, so that the logistics path cannot be optimized, meanwhile, mechanical energy data analysis, prediction and risk management cannot be performed, and logistics tracking management is not facilitated.

The invention solves the technical problems through the following technical scheme that the artificial intelligent logistics freight tracking computing terminal comprises an inventory management module, a path planning module, a transportation optimization module, a supply chain coordination module, a real-time monitoring module, a data analysis and prediction module and a risk management module, wherein the inventory management module, the path planning module, the transportation optimization module, the supply chain coordination module, the real-time monitoring module, the data analysis and prediction module and the risk management module are in communication connection;

the inventory management module is used for managing inventory of goods, and comprises warehousing, ex-warehouse and inventory, and information acquired through the real-time monitoring module;

the path planning module is used for calculating the shortest path or the optimal path according to the starting point and the finishing point of the goods and the traffic condition factors of the road;

the transportation optimization module is used for calculating an optimal cargo distribution scheme according to the quantity, the volume and the weight of cargoes and the capacity, the speed and the cost of transportation means;

the supply chain cooperative module is used for cooperatively managing various links in the supply chain, including production, storage and transportation links;

the real-time monitoring module is used for acquiring the position, state and temperature information of goods through logistics transportation equipment (such as GPS and sensors) and carrying out real-time monitoring and early warning;

the data analysis and prediction module is used for analyzing and predicting the trend and characteristics of the cargo transportation, including cargo quantity, transportation distance, transportation time and cost factors;

the risk management module is used for calculating the possibility of losing goods under specific conditions by using a conditional probability formula in probability theory, so that risk control is facilitated.

Preferably, the path planning module uses Dijkstra algorithm to calculate, and the algorithm is specifically as follows:

d (i) $: shortest distance from origin to node $i$;

w (i, j) $: the distance between node $i$ to node $j$;

$ P (i) $: the previous node of node $i$;

$ S $: the node set that has found the shortest path;

$ V-S $: node sets for which the shortest path is not found;

step 1, initializing: d (S) =0$, $d (i) = \infty$, $p (i) =s$, $s= \emptyset$, $v-s=v$;

step 2, selecting a node $k with the minimum $D from $V-S $, and adding the node $k into $S $;

step 3, for each neighbor node $j$ of the node $k$, if $D (j) > D (k) +w (k, j) $, updating the values of $D (j) $ and $P (j) $;

step 4, repeat steps 2 and 3 until all nodes join in $s$, or there are no joinable nodes.

Preferably, the optimization model of the transportation optimization module is as follows:

objective function: maxz=c_1x_1+c_2x_2+ + c_nx_n$

Constraint conditions: 11} x_1+a_12 } x_2+, +a_1 n } x_n_leb_1 $

$a_{21}x_1+a_{22}x_2+...+a_{2n}x_n\leb_2$

$...$

$a_{m1}x_1+a_{m2}x_2+...+a_{mn}x_n\leb_m$

$x_1,x_2,...,x_n\ge0$

Where $ x_i $ represents the number of items of the $ i $ th item, $ c_i $ represents the unit transportation cost of items of the $ i $ th item, $ a $ ij $ represents the limit of the amount of transportation of items of the $ i $ th item on the $ j $ th item of transportation means, $ b_j $ represents the limit of the amount of transportation capacity of items of the $ j $ th item, the objective function is to minimize the total transportation cost, and the constraint is to ensure that the amount of transportation does not exceed the limit.

Preferably, the probability management module calculates the probability of the cargo being lost in a specific situation by using a conditional probability formula in the probability theory, and if the event a represents a specific risk situation and the event B represents the cargo loss, the conditional probability formula can be expressed as:

P(B|A)＝P(AandB)/P(A)

wherein P (b|a) represents the probability of occurrence of event B in the event of occurrence of event a; p (AandB) represents the probability of event a and event B occurring simultaneously; p (a) represents the probability of event a occurring.

Preferably, in the data analysis and prediction module, the trend and characteristic of the cargo transportation are analyzed by using a regression analysis model, the regression analysis model uses a least square method to perform parameter estimation, and the model is:

y＝b0+b1*x1+b2*x2+...+bk*xk+e

where y represents the dependent variable, x1, x2,., xk represents the independent variable, b0, b1, b2,., bk represents the regression coefficient, and e represents the error term.

Compared with the prior art, the invention has the following advantages:

the path planning module is matched with the transportation optimizing module, wherein the path planning module can calculate the shortest path or the optimal path according to factors such as the starting point and the end point of cargoes, the traffic condition of roads and the like, and the transportation optimizing module can calculate the optimal cargo distribution scheme according to factors such as the quantity, the volume, the weight and the like of the cargoes, the capacity, the speed, the cost and the like of transportation means, so that the cost and the efficiency of the whole logistics path and transportation are greatly improved;

by setting a data analysis and prediction module, a regression analysis model is adopted, so that the relation between independent variables and dependent variables can be analyzed and predicted, and the relation between the quantity of cargoes and the transportation distance can be analyzed by utilizing historical data, so that the future quantity of cargoes and the transportation distance can be predicted, a basis is provided for cargo transportation decision, and the trend and characteristics of cargo transportation can be analyzed and predicted, including factors such as the quantity of cargoes, the transportation distance, the transportation time, the cost and the like;

the risk management module is arranged, a conditional probability formula in the probability theory is used in the risk management module to calculate the probability of loss of the goods under specific conditions, and the probability of loss of the goods under specific conditions can be calculated according to historical data and risk assessment results, so that corresponding risk management measures are formulated.

Drawings

Fig. 1 is a system block diagram of the present invention.

Detailed Description

The following describes in detail the examples of the present invention, which are implemented on the premise of the technical solution of the present invention, and detailed embodiments and specific operation procedures are given, but the scope of protection of the present invention is not limited to the following examples.

As shown in fig. 1, this embodiment provides a technical solution: an artificial intelligent logistics freight tracking computing terminal comprises an inventory management module, a path planning module, a transportation optimization module, a supply chain cooperation module, a real-time monitoring module, a data analysis and prediction module and a risk management module, wherein the inventory management module, the path planning module, the transportation optimization module, the supply chain cooperation module, the real-time monitoring module, the data analysis and prediction module and the risk management module are in communication connection;

the inventory management module is used for managing the inventory of goods, including warehousing, ex-warehouse and inventory, and the information obtained by the real-time monitoring module;

the path planning module is used for calculating the shortest path or the optimal path according to the starting point and the ending point of the goods and the traffic condition factors of the road;

the transportation optimization module is used for calculating an optimal cargo distribution scheme according to the quantity, the volume and the weight of cargoes and the capacity, the speed and the cost of a transportation tool;

the supply chain cooperative module is used for cooperatively managing various links in the supply chain, including production, storage and transportation links;

the real-time monitoring module is used for acquiring the position, state and temperature information of the goods through logistics transportation equipment (such as GPS and sensors) and carrying out real-time monitoring and early warning;

the data analysis and prediction module is used for analyzing and predicting the trend and characteristics of the cargo transportation, including cargo quantity, transportation distance, transportation time and cost factors;

the risk management module is used for calculating the possibility of losing goods under specific conditions by using a conditional probability formula in the probability theory, so that risk control is facilitated.

The path planning module calculates by using Dijkstra algorithm, and the algorithm is specifically as follows:

d (i) $: shortest distance from origin to node $i$;

w (i, j) $: the distance between node $i$ to node $j$;

$ P (i) $: the previous node of node $i$;

$ S $: the node set that has found the shortest path;

$ V-S $: node sets for which the shortest path is not found;

step 1, initializing: d (S) =0$, $d (i) = \infty$, $p (i) =s$, $s= \emptyset$, $v-s=v$;

step 2, selecting a node $k with the minimum $D from $V-S $, and adding the node $k into $S $;

step 3, for each neighbor node $j$ of the node $k$, if $D (j) > D (k) +w (k, j) $, updating the values of $D (j) $ and $P (j) $;

step 4, repeat steps 2 and 3 until all nodes join in $s$, or there are no joinable nodes.

The optimization model of the transport optimization module is as follows:

objective function: maxz=c_1x_1+c_2x_2+ + c_nx_n$

Constraint conditions: 11} x_1+a_12 } x_2+, +a_1 n } x_n_leb_1 $

$a_{21}x_1+a_{22}x_2+...+a_{2n}x_n\leb_2$

$...$

$a_{m1}x_1+a_{m2}x_2+...+a_{mn}x_n\leb_m$

$x_1,x_2,...,x_n\ge0$

Where $ x_i $ represents the number of items of the $ i $ th item, $ c_i $ represents the unit transportation cost of items of the $ i $ th item, $ a $ ij $ represents the limit of the amount of transportation of items of the $ i $ th item on the $ j $ th item of transportation means, $ b_j $ represents the limit of the amount of transportation capacity of items of the $ j $ th item, the objective function is to minimize the total transportation cost, and the constraint is to ensure that the amount of transportation does not exceed the limit.

Using a conditional probability formula in the probability theory in the risk management module to calculate the probability that the cargo is lost in a specific situation, setting event a to represent a specific risk situation and event B to represent the cargo loss, then the conditional probability formula can be expressed as:

P(B|A)＝P(AandB)/P(A)

wherein P (b|a) represents the probability of occurrence of event B in the event of occurrence of event a; p (AandB) represents the probability of event a and event B occurring simultaneously; p (a) represents the probability of event a occurring.

In the data analysis and prediction module, the trend and the characteristics of cargo transportation are analyzed by using a regression analysis model, the regression analysis model uses a least square method to carry out parameter estimation, and the model is as follows:

y＝b0+b1*x1+b2*x2+...+bk*xk+e

where y represents the dependent variable, x1, x2,., xk represents the independent variable, b0, b1, b2,., bk represents the regression coefficient, and e represents the error term.

In summary, when the invention is used, when the goods are in the warehouse time or the time to be warehouse, the inventory roller module cooperates with the real-time monitoring module to warehouse in, warehouse out and inventory the goods, the real-time monitoring module obtains information through the real-time monitoring module, and simultaneously, when the goods are in the logistics process, the real-time monitoring module obtains the position, state and temperature information of the goods through logistics transportation equipment such as GPS and sensors, real-time monitoring and early warning are carried out, the path optimization module calculates the most available path according to Dijkstra algorithm, and meanwhile, the transportation optimization module carries out optimized transportation, and the data analysis and prediction module and the risk management module can carry out data analysis prediction and risk management on the logistics and the goods in the logistics tracking process.

While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.

Claims (5)

1. The artificial intelligent logistics freight tracking and calculating terminal is characterized by comprising an inventory management module, a path planning module, a transportation optimization module, a supply chain coordination module, a real-time monitoring module, a data analysis and prediction module and a risk management module, wherein the inventory management module, the path planning module, the transportation optimization module, the supply chain coordination module, the real-time monitoring module, the data analysis and prediction module and the risk management module are in communication connection;

the inventory management module is used for managing inventory of goods, and comprises warehousing, ex-warehouse and inventory, and information acquired through the real-time monitoring module;

the path planning module is used for calculating the shortest path or the optimal path according to the starting point and the finishing point of the goods and the traffic condition factors of the road;

the transportation optimization module is used for calculating an optimal cargo distribution scheme according to the quantity, the volume and the weight of cargoes and the capacity, the speed and the cost of transportation means;

the supply chain cooperative module is used for cooperatively managing various links in the supply chain, including production, storage and transportation links;

the real-time monitoring module is used for acquiring the position, state and temperature information of goods through logistics transportation equipment (such as GPS and sensors) and carrying out real-time monitoring and early warning;

the data analysis and prediction module is used for analyzing and predicting the trend and characteristics of the cargo transportation, including cargo quantity, transportation distance, transportation time and cost factors;

the risk management module is used for calculating the possibility of losing goods under specific conditions by using a conditional probability formula in probability theory, so that risk control is facilitated.

2. The artificial intelligence logistics freight tracking computing terminal of claim 1, wherein: the path planning module adopts Dijkstra algorithm to calculate, and the algorithm is specifically as follows:

d (i) $: shortest distance from origin to node $i$;

w (i, j) $: the distance between node $i$ to node $j$;

$ P (i) $: the previous node of node $i$;

$ S $: the node set that has found the shortest path;

$ V-S $: node sets for which the shortest path is not found;

step 1, initializing: d (S) =0$, $d (i) = \infty$, $p (i) =s$, $s= \emptyset$, $v-s=v$;

step 2, selecting a node $k with the minimum $D from $V-S $, and adding the node $k into $S $;

step 3, for each neighbor node $j$ of the node $k$, if $D (j) > D (k) +w (k, j) $, updating the values of $D (j) $ and $P (j) $;

step 4, repeat steps 2 and 3 until all nodes join in $s$, or there are no joinable nodes.

3. The artificial intelligence logistics freight tracking computing terminal of claim 1, wherein: the optimization model of the transportation optimization module is as follows:

objective function: maxz=c_1x_1+c_2x_2+ + c_nx_n$

Constraint conditions: 11} x_1+a_12 } x_2+, +a_1 n } x_n_leb_1 $

$a_{21}x_1+a_{22}x_2+...+a_{2n}x_n\leb_2$

$...$

$a_{m1}x_1+a_{m2}x_2+...+a_{mn}x_n\leb_m$

$x_1,x_2,...,x_n\ge0$

Where $ x_i $ represents the number of items of the $ i $ th item, $ c_i $ represents the unit transportation cost of items of the $ i $ th item, $ a $ ij $ represents the limit of the amount of transportation of items of the $ i $ th item on the $ j $ th item of transportation means, $ b_j $ represents the limit of the amount of transportation capacity of items of the $ j $ th item, the objective function is to minimize the total transportation cost, and the constraint is to ensure that the amount of transportation does not exceed the limit.

4. The artificial intelligence logistics freight tracking computing terminal of claim 1, wherein: using a conditional probability formula in probability theory in the risk management module to calculate the probability of the cargo being lost in a specific situation, setting event a to represent a specific risk situation and event B to represent the cargo loss, then the conditional probability formula can be expressed as:

P(B|A)＝P(AandB)/P(A)

wherein P (b|a) represents the probability of occurrence of event B in the event of occurrence of event a; p (AandB) represents the probability of event a and event B occurring simultaneously; p (a) represents the probability of event a occurring.

5. The artificial intelligence logistics freight tracking computing terminal of claim 1, wherein: in the data analysis and prediction module, a regression analysis model is used for analyzing the trend and the characteristics of cargo transportation, and the regression analysis model uses a least square method for parameter estimation, and the model is as follows:

y＝b0+b1*x1+b2*x2+...+bk*xk+e

where y represents the dependent variable, x1, x2,., xk represents the independent variable, b0, b1, b2,., bk represents the regression coefficient, and e represents the error term.